Nonlinear Analysis of Electrocardiography Signals for Atrial Fibrillation
This paper aims to analyze the electrocardiography (ECG) signals for patient with atrial fibrillation (AF) by using bispectrum and extreme learning machine (ELM). AF is the most common irregular heart beat disease which may cause many cardiac diseases as well. Bispectral analysis was used to extract...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/509784 |
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| _version_ | 1832563118929084416 |
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| author | Necmettin Sezgin |
| author_facet | Necmettin Sezgin |
| author_sort | Necmettin Sezgin |
| collection | DOAJ |
| description | This paper aims to analyze the electrocardiography (ECG) signals for patient with atrial fibrillation (AF) by using bispectrum and extreme learning machine (ELM). AF is the most common irregular heart beat disease which may cause many cardiac diseases as well. Bispectral analysis was used to extract the nonlinear information in the ECG signals. The bispectral features of each ECG episode were determined and fed to the ELM classifier. The classification accuracy of ELM to distinguish nonterminating, terminating AF, and terminating immediately AF was 96.25%. In this study, the normal ECG signal was also compared with AF ECG signal due to the nonlinearity which was determined by bispectrum. The classification result of ELM was 99.15% to distinguish AF ECGs from normal ECGs. |
| format | Article |
| id | doaj-art-d9f63572bc304f819285a94e03d6f2d8 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-d9f63572bc304f819285a94e03d6f2d82025-02-03T01:20:57ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/509784509784Nonlinear Analysis of Electrocardiography Signals for Atrial FibrillationNecmettin Sezgin0Department of Electrical and Electronics Engineering, Faculty of Architecture and Engineering, Batman University, 72060 Batman, TurkeyThis paper aims to analyze the electrocardiography (ECG) signals for patient with atrial fibrillation (AF) by using bispectrum and extreme learning machine (ELM). AF is the most common irregular heart beat disease which may cause many cardiac diseases as well. Bispectral analysis was used to extract the nonlinear information in the ECG signals. The bispectral features of each ECG episode were determined and fed to the ELM classifier. The classification accuracy of ELM to distinguish nonterminating, terminating AF, and terminating immediately AF was 96.25%. In this study, the normal ECG signal was also compared with AF ECG signal due to the nonlinearity which was determined by bispectrum. The classification result of ELM was 99.15% to distinguish AF ECGs from normal ECGs.http://dx.doi.org/10.1155/2013/509784 |
| spellingShingle | Necmettin Sezgin Nonlinear Analysis of Electrocardiography Signals for Atrial Fibrillation The Scientific World Journal |
| title | Nonlinear Analysis of Electrocardiography Signals for Atrial Fibrillation |
| title_full | Nonlinear Analysis of Electrocardiography Signals for Atrial Fibrillation |
| title_fullStr | Nonlinear Analysis of Electrocardiography Signals for Atrial Fibrillation |
| title_full_unstemmed | Nonlinear Analysis of Electrocardiography Signals for Atrial Fibrillation |
| title_short | Nonlinear Analysis of Electrocardiography Signals for Atrial Fibrillation |
| title_sort | nonlinear analysis of electrocardiography signals for atrial fibrillation |
| url | http://dx.doi.org/10.1155/2013/509784 |
| work_keys_str_mv | AT necmettinsezgin nonlinearanalysisofelectrocardiographysignalsforatrialfibrillation |